| 基于神经网络对裂纹扩展过程的预测 |
| 投稿时间：2021-08-16 修订日期：2021-10-21 点此下载全文 |
| 引用本文：郑国君,杜超群,申国哲,夏阳.基于神经网络对裂纹扩展过程的预测[J].计算机辅助工程,2021,30(4):. |
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| 基金项目:中国自然科学基金项目(No. 12072065)，中央高校基本科研业务费专项资金(DUT20JC34) |
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| 中文摘要:针对近场动力学模型计算时间较长的问题，提出了基于神经网络对裂纹扩展过程实时预测的方法，提高了计算效率。该方法基于近场动力学算法获取裂纹扩展过程中的损伤云图，并构建生成对抗网络模型的数据集，根据加载条件实时生成损伤云图，从而快速预测裂纹的扩展过程。将近场动力学模型计算得到的损伤云图中的R, G, B值和相应位置处的损伤值结合，构建多层前馈神经网络模型的数据集，根据生成对抗网络模型生成的损伤云图中的R, G, B值计算相应的损伤值。本文所提出的方法提高了计算效率，其计算结果与近场动力学算法的计算结果吻合。 |
| 中文关键词:近场动力学 计算时间 生成对抗网络 裂纹扩展 裂纹预测 损伤云图 前馈神经网络 损伤值预测 |
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| Prediction of crack propagation process based on neural network |
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| Abstract:The calculation time of the peridynamic (PD) model is too long, a method of real-time prediction of the crack propagation process based on the neural network was proposed to solve this problem, which improves the calculation efficiency. Based on the PD model, the damage contours of the crack propagation process were obtained, and a dataset of the generative adversarial network model were constructed and generated. Then, the damage contours were generated in real time according to differential loading conditions, thus the crack propagation process can be predicted quickly. The dataset of the multi-layer feedforward neural network model was constructed based on the R, G, B color values and the damage values from the damage contour generated by the PD model. finally, the damage values were calculated by the R, G, B color values based on the multi-layer feedforward neural network model. The proposed method is in good agreement with the calculation results generated by the PD method, and improves the computational efficiency. |
| keywords:peridynamics calculation time generative adversarial network crack propagation crack prediction damage contours feedforward neural network damage value prediction |
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